Traffic Graph Convolutional Network for Dynamic Urban Travel Speed Estimation

The dynamic urban link travel speed estimation (DU-LSE) problem has been studied extensively with approaches ranging from model to data driven since it benefits multiple applications in transport mobility, especially in dense cities. However, with drawbacks such as heavy assumption in model-driven and not being capable for big city network in data-driven, there has not been a consensus on the most effective method. This study aims to develop a Sequential Three Step framework to solve the DU-LSE problem using only the passively collected taxi trip data. The framework makes use of two deep learning models namely Traffic Graph Convolution (TGCN) and its recurrent variant TGCNlstm to capture both spatial and temporal correlation between road segments. The proposed framework has three advantages over similar approaches: (1) it uses only the affordable taxi data and overcomes the data’s incompleteness both in spatial (full GPS trajectory is not available) and temporal (incomplete historic time-series) domain, (2) it is specifically designed to preserve the directionality nature of traffic flow, and (3) it is capable for large networks. The model results and validations suggest the framework can achieve high enough accuracy and will provide valuable mobility data for cities especially those without traffic sensing infrastructure already in place.